Training needs assessment of extension workers for delivering climate-smart agriculture advisory services

Introduction

When Mali's first post-independence extension service was established in the early 1960s, the advisory mandate was straightforward: teach farmers to grow cotton and millet using improved seeds and fertilizer [1] . Six decades later, the challenge has changed beyond recognition. Rainfall patterns have shifted, growing seasons have shortened by two to three weeks in southern Mali since the 1980s, and pest pressures have intensified in ways that fixed recommendation packages cannot address [2] . Climate-smart agriculture offers a framework that aims to increase productivity, strengthen resilience, and reduce greenhouse gas emissions simultaneously [3] . But translating CSA principles into practical field advice requires extension workers who understand climate science basics, can interpret weather forecasts, and are familiar with technologies like agroforestry, conservation tillage, and drought-tolerant varieties [4] . Evidence from across sub-Saharan Africa suggests that most frontline extension staff lack these competencies [5,6] . Training Needs Assessment (TNA) provides a systematic way to identify gaps between what workers know and what they need to know to perform effectively [7] . While TNA methods are well established in human resource development, their application to agricultural extension in the context of climate change remains limited, particularly in francophone West Africa [8] . Mali's extension system, managed through regional agricultural offices, faces chronic understaffing and budget constraints that make targeted training investments especially important [9] . This research assessed the CSA-related training needs of extension workers in three regions of southern Mali by comparing current competency levels with expert-defined requirements. It also tested whether worker characteristics predicted competency levels and proposed a training framework to guide capacity-building investments.

Study area description

The research was conducted in three regions of southern Mali: Sikasso, Koulikoro, and Ségou. These regions form the country's primary agricultural zone, receiving between 600 and 1,200 mm of annual rainfall and supporting crops including millet, sorghum, maize, cotton, and groundnuts. Average temperatures range from 25 °C to 35 °C across the growing season (June-October). The three regions collectively employ approximately 420 extension workers serving an estimated 1.8 million farming households. Extension services are delivered through regional agricultural directorates with support from international development partners.

Material and Methods

Material

A total of 148 extension workers were selected using stratified random sampling from personnel registries of the three regional agricultural directorates between April and www.extensionjournal.com October 2023. Stratification was by region and years of experience. A structured questionnaire measured selfassessed competency across seven CSA domains on a fivepoint scale (1 = no knowledge, 5 = expert level). Required competency levels for each domain were established through a prior Delphi consultation with 12 CSA specialists from Malian research institutions and international organizations. Worker demographic data, training history, and educational background were also collected. Ethical clearance was obtained from the Institut Polytechnique Rural de Formation et de Recherche Appliquee Ethics Committee (Protocol IPR-EC-2023-008, March 2023).

Methods

Training need gaps were calculated as the difference between required and current competency for each domain.

Descriptive statistics summarized worker characteristics and gap scores. Multiple regression tested whether age, education, experience, and prior CSA training predicted overall competency scores. Data were analyzed using SPSS 28. The significance level was set at p<0.05. Gap scores were ranked to identify priority training areas.

Ethical considerations

All participants provided written informed consent after receiving explanations in both French and Bambara. Participation was voluntary, and workers were assured that responses would not affect employment evaluations. Questionnaires were anonymized using numeric codes. Data were stored on encrypted devices accessible only to the research team. Regional directorate heads were informed of the research but did not receive individual-level results. The framework organizes training content into knowledge, skills, and attitude pillars. Each pillar contains three priority modules identified from the gap analysis and informant consultations. The structure allows modular delivery suited to the resource constraints of Mali's extension system.

Results

Discussion

The finding that 87.2% of extension workers lacked any formal CSA training is striking but not surprising given the chronic resource shortages facing Mali's agricultural extension system [9] . What the gap analysis adds is specificity -it identifies exactly where the deficits are largest and where training investments would yield the greatest returns. Weather information interpretation ranked as the top priority, which aligns with research showing that farmers in the Sahel are increasingly exposed to seasonal www.extensionjournal.com forecast information but receive little help from extension agents in translating those forecasts into management decisions [10,11] . The relatively smaller gap in soil and water conservation likely reflects two decades of donor-supported programs targeting erosion control and water harvesting in southern Mali [12] . This suggests that sustained training investment can close competency gaps, but it needs to be extended to newer CSA domains like agroforestry and carbon management. The strong predictive effect of prior CSA training (β = 0.39) reinforces the straightforward point that workers who receive training perform better -a finding consistent with human capital theory and with extension training evaluations elsewhere in West Africa [13,14] . The proposed three-pillar framework is designed for practicality. Rather than a single intensive course, it allows modular delivery where individual pillars can be covered in three-to-five-day blocks spread across a growing season. This approach respects the reality that extension workers cannot be absent from their field posts for extended periods [15] .

Practical applications

Training should begin with weather data interpretation and agroforestry, the two domains with the largest gaps and highest practical demand. Regional directorates can partner with national meteorological services to develop simplified forecast interpretation guides for extension use. Agroforestry modules should include hands-on demonstration plot establishment, as workers reported low confidence with practical techniques despite some theoretical awareness. Training budgets should target workers with no prior CSA exposure first, as they represent the largest and most underserved group.

Conclusion

Extension workers in southern Mali face large competency gaps across all seven CSA domains examined, with weather information interpretation and carbon sequestration registering the widest deficits. The near-total absence of formal CSA training among the workforce points to a structural investment gap rather than individual shortcomings. The three-pillar training framework proposed here offers a modular, field-compatible structure that fits within the operational constraints of Mali's extension system. But training alone won't close the gap. Workers also need access to updated technical materials in local languages, reliable weather data feeds, and supportive supervision that reinforces new competencies after training ends. Investing in CSA capacity for extension workers is not optional if Mali's agricultural sector is to adapt to worsening climate pressures. The gap analysis presented here gives planners a concrete basis for prioritizing limited training resources where they are most needed.

References

1. Bingen RJ. Cotton, democracy and development in Mali. J Mod Afr Stud. 1998;36(2):265-285.
2. Sultan B, Gaetani M. Agriculture in West Africa in the twenty-first century: Climate change and impacts scenarios, and potential for adaptation. Glob Planet Change. 2016;138:1-12.
3. FAO. Climate-smart agriculture sourcebook. Rome: Food and Agriculture Organization; 2013.
4. Neufeldt H, Jahn M, Campbell B, Beddington J, DeClerck F, De Pinto A, et al. Beyond climate-smart agriculture: Toward safe operating spaces for global food systems. Agric Food Secur. 2013;2(12):1-6.
5. Nyasimi M, Kimeli P, Sayula G, Radeny M, Kinyangi J. Training and awareness of climate change among agricultural extension workers in East Africa. J Agric Educ Ext. 2017;23(1):67-81.
6. Murage AW, Pittchar JO, Midega CAO, Onyango CO, Khan ZR. Gender-specific perceptions and adaptation strategies to climate change in Western Kenya. Clim Dev. 2015;7(4):361-371.
7. Borich GD. A needs assessment model for conducting follow-up studies. J Teach Educ. 1980;31(3):39-42.
8. Blundo-Canto G, Andrieu N, Soule-Adam Y, Hainzelin E. Farmer perceptions and adaptation strategies to climate change in Mali. Reg Environ Change. 2018;18(4):1091-1103.
9. Dembele I, Kaminski J. Mali agricultural sector review. Washington (DC): World Bank; 2019.
10. Hansen JW, Mason SJ, Sun L, Tall A. Review of seasonal climate forecasting for agriculture in subSaharan Africa. Exp Agric. 2011;47(2):205-240.
11. Carr ER, Owusu-Daaku KN. The shifting epistemologies of vulnerability in climate services for development: The case of Mali's agrometeorological advisory programme. Area. 2016;48(1):65-72.
12. Winterbottom R, Reij C, Garrity D, Glover J, Hellums D, McGahuey M, et al. Improving land and water management. Washington (DC): World Resources Institute; 2013.
13. Davis K, Nkonya E, Kato E, Mekonnen DA, Odendo M, Miiro R, et al. Impact of farmer field schools on agricultural productivity and poverty in East Africa. World Dev. 2012;40(2):402-413.
14. Beaman L, BenYishay A, Magruder J, Mobarak AM. Can network theory explain the diffusion of agricultural technology? Am Econ Rev. 2021;111(6):1918-1943.
15. Simpson BM, Burpee CG. Adaptation under the new normal of climate change: The future of agricultural extension and advisory services. CCAFS Working Paper No. 83. Copenhagen: CGIAR; 2014.
16. Bie SW, Kristjanson P, Barham BL. Agricultural extension reform in developing countries. Agric Hum Values. 2006;23(3):261-273.
17. Kibett JK, Omunyin ME, Muchiri J. Extension agent competency needs for effective agricultural extension service delivery. J Agric Ext. 2012;16(1):22-34.
18. Lambrecht I, Vanlauwe B, Merckx R, Maertens M. Understanding the process of agricultural technology adoption: Scaling up soil fertility management in subSaharan Africa. Agric Syst. 2016;142:47-60.
19. Thornton PK, Ericksen PJ, Herrero M, Challinor AJ. Climate variability and vulnerability to climate change: A review. Glob Change Biol. 2014;20(11):3313-3328.
20. Teklewold H, Kassie M, Shiferaw B. Adoption of multiple climate-smart agricultural practices in rural Ethiopia. Food Policy. 2013;40:1-10.
21. Partey ST, Zougmoré RB, Ouedraogo M, Campbell BM. Developing climate-smart agriculture training modules for extension agents. Clim Risk Manag. 2018;22:13-19.